Vertical Federated Learning (VFL) enables organizations with disjoint feature spaces but shared user bases to collaboratively train models without sharing raw data. However, existing VFL systems face critical limitations: they often lack effective incentive mechanisms, struggle to balance privacy-utility tradeoffs, and fail to accommodate clients with heterogeneous resource capabilities. These challenges hinder meaningful participation, degrade model performance, and limit practical deployment. To address these issues, we propose OPUS-VFL, an Optimal Privacy-Utility tradeoff Strategy for VFL. OPUS-VFL introduces a novel, privacy-aware incentive mechanism that rewards clients based on a principled combination of model contribution, privacy preservation, and resource investment. It employs a lightweight leave-one-out (LOO) strategy to quantify feature importance per client, and integrates an adaptive differential privacy mechanism that enables clients to dynamically calibrate noise levels to optimize their individual utility. Our framework is designed to be scalable, budget-balanced, and robust to inference and poisoning attacks. Extensive experiments on benchmark datasets (MNIST, CIFAR-10, and CIFAR-100) demonstrate that OPUS-VFL significantly outperforms state-of-the-art VFL baselines in both efficiency and robustness. It reduces label inference attack success rates by up to 20%, increases feature inference reconstruction error (MSE) by over 30%, and achieves up to 25% higher incentives for clients that contribute meaningfully while respecting privacy and cost constraints. These results highlight the practicality and innovation of OPUS-VFL as a secure, fair, and performance-driven solution for real-world VFL.

本研究解决了垂直联邦学习（VFL）系统中缺乏有效激励机制、隐私与效用权衡不足以及客户端资源异质性的挑战。提出的OPUS-VFL引入了一种新颖的隐私感知激励机制，根据模型贡献、隐私保护和资源投资进行奖励。实验结果显示，OPUS-VFL在效率和鲁棒性上显著优于现有的VFL基线，展示了其在实际应用中的创新性和实用性。

OPUS-VFL：激励垂直联邦学习中的最佳隐私-效用权衡